Peer-reviewed veterinary case report
Machine learning helps diagnose sinus node dysfunction in dogs
By Flanders, Wyatt Hutson et al.·Published in Journal of veterinary internal medicine·2024·Department of Clinical Sciences, United States·View original on PubMed →
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Original publication title: Use of machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs.
- Species:
- dog
Plain-English summary
A group of dogs with heart issues were studied to understand sinus node dysfunction, which can cause slow heart rates (bradycardia). Researchers found that the number and length of pauses in heartbeats were key indicators of this condition, distinguishing it from other heart rate variations caused by nervous system influences. Using advanced computer techniques, they were able to accurately diagnose sinus node dysfunction by analyzing these pauses. This research suggests that specific heart rate patterns can help veterinarians identify this condition more effectively, leading to better treatment options for affected dogs.
People also search for: dog slow heart rate treatment · sinus node dysfunction in dogs · bradycardia causes in dogs
Abstract
BACKGROUND: Sinus node dysfunction because of abnormal impulse generation or sinoatrial conduction block causes bradycardia that can be difficult to differentiate from high parasympathetic/low sympathetic modulation (HP/LSM). HYPOTHESIS: Beat-to-beat relationships of sinus node dysfunction are quantifiably distinguishable by Poincaré plots, machine learning, and 3-dimensional density grid analysis. Moreover, computer modeling establishes sinoatrial conduction block as a mechanism. ANIMALS: Three groups of dogs were studied with a diagnosis of: (1) balanced autonomic modulation (n = 26), (2) HP/LSM (n = 26), and (3) sinus node dysfunction (n = 21). METHODS: Heart rate parameters and Poincaré plot data were determined [median (25%-75%)]. Recordings were randomly assigned to training or testing. Supervised machine learning of the training data was evaluated with the testing data. The computer model included impulse rate, exit block probability, and HP/LSM. RESULTS: Confusion matrices illustrated the effectiveness in diagnosing by both machine learning and Poincaré density grid. Sinus pauses >2 s differentiated (P < .0001) HP/LSM (2340; 583-3947 s) from sinus node dysfunction (8503; 7078-10 050 s), but average heart rate did not. The shortest linear intervals were longer with sinus node dysfunction (315; 278-323 ms) vs HP/LSM (260; 251-292 ms; P = .008), but the longest linear intervals were shorter with sinus node dysfunction (620; 565-698 ms) vs HP/LSM (843; 799-888 ms; P < .0001). CONCLUSIONS: Number and duration of pauses, not heart rate, differentiated sinus node dysfunction from HP/LSM. Machine learning and Poincaré density grid can accurately identify sinus node dysfunction. Computer modeling supports sinoatrial conduction block as a mechanism of sinus node dysfunction.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/38682817/